It is difficult to develop artificial intelligence expert systems for screening. FIG. 1A is a flow chart 100 of a prior art method for developing an artificial intelligence expert system for screening candidates for employment. It is based on FIG. 5 of U.S. Pat. No. 7,080,057 “Electronic Employee Selection Systems and Methods” (Scarborough). The method comprises collecting pre-hire information 102, collecting post-hire information 104, and building a model 106. The method then refines pre-hire content 108 and repeats the steps. FIG. 1B is an illustration of a neural net model 110 generated by this process. FIG. 1B is based on FIG. 10 of Scarborough. The neural net comprises input items 112, weights for said input items 118, hidden layer nodes 114 and an output 116.
One of the drawbacks of the Scarborough expert system is that it requires large quantities of high quality pre-hire and post-hire data that have to be collected over a long period of time. This is primarily due to the large number of parameters in neural nets that have to be calculated using the data. The weights for each input item, for example, need to be calculated as well as the weights for each neural net node. In example 35 of Scarborough, 2084 complete employment records collected over a year and a half were required to calculate said weights. Even then, the model was still subject to over-training. The Scarborough expert system, therefore, will not work for smaller organizations that might have only 100 persons or less in a given task function. There isn't enough data from current persons in these small organizations to calculate the parameters in the model without overtraining. There is need, therefore, for an artificial intelligence expert system for screening that can be developed with data from only a small number of current persons in a given task function.